## Samples.Var1 Samples.Freq
## 1 3PGMouseControl 3
## 2 Blank 2
## 3 Control 60
## 4 LRRK2_R1441C 60
## 5 SingleCellLysateCtrl 3
## Samples are in the correct order
## 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128
## summarizing abundance
## summarizing counts
## summarizing length
We used kallisto (version kallisto_linux-v0.43.0) to create a reference index and estimate transcript abundances.
We downloaded the human reference transcriptome from Ensembl (release 95) including cdna and ncrna sequences:
ftp://ftp.ensembl.org/pub/release-95/fasta/homo_sapiens/cdna/Homo_sapiens.GRCh38.cdna.all.fa.gz ftp://ftp.ensembl.org/pub/release-95/fasta/homo_sapiens/ncrna/Homo_sapiens.GRCh38.ncrna.fa.gz
We removed sequences in scaffolds and kept only chromosomes 1, 2, …, 22, X and Y.
We have included ERCC sequences when creating the reference index (ERCC.fa.gz)
All annotations from transcript and genes are in the following file: “Annotation_Homo_sapiens.GRCh38.cdna.ncrna.rmCHR.txt”.
Features with zero expression across all samples have been removed.
Gene-level TPM (transcripts per million) estimates represent the overall transcriptional output of each gene
We filtered out 2 sets: one including all gene biotypes All_biotypes, and the other including only protein coding genes, plus control features (ribo.genes, mito.genes, ERCCs) PCMRE
Sum of the raw counts per gene biotype.
The sums are then log transformed log10(counts + 1).
## Warning in .local(object, ...): using library sizes as size factors
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Project 128
Genome 128
patient_ID 60 60
sex 120
reprogramming 120
WellLocation 2 2 2 2 1 1 1 1 1 1 1 1 2 2 2 2 1 1 1 1 1 1 1 1 2 2 2 2 1 1 1 1 1 1 1 1 2 2 2 2 1 1 1 1 1 1 1 1 2 2 2 2 1 1 1 1 1 1 1 1 2 2 2 2 1 1 1 1 1 1 1 1 2 2 2 2 1 1 1 1 1 1 1 1 2 2 2 2 1 1 1 1 1 1 1 1
is_cell_control 120 8
is_cell_control_control 120 8
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Clustering on PC1 to remove low quality cells.
We use kmeans clustering with the Euclidean distance of PC1 (k = 2).
Also removed: control cells (SingleCellLysateCtrl, 3PGMouseControl and Blank) and cells with an outlier number of total features (total_features_by_counts)
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##
## 3PGMouseControl Blank Control
## 3 1 41
## LRRK2_R1441C SingleCellLysateCtrl
## 32 3
Filter out lowly expressed genes. Keep genes expressed in at least 20% of the 72 possible cells (i.e. non zero expression in at least 14.4 cells).
We also remove ERCC controls at this point.
We end up with 72 cells and 6789
The average number of features per cell 3605.4861111
We look for the expression of the following dopamineric markers: TH, DDC, SLC6A3, SLC18A2, DRD2, SLC18A2, LMX1A, LMX1B, FOXA2, NR4A2, PITX3, EN1, EN2.
Only the ones below were kept after filtering.
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## ENSG00000180176.14 ENSG00000132437.17 ENSG00000165646.13
## 33 33 20
## ENSG00000162761.14 ENSG00000153234.13
## 19 16
In addition of dopaminergic markers, here we show glutamatergic markers: CTIP2 / BCL11B, KA1 / GRIK4, NMDAR1 / GRIN1, OTX1, TBR1.
We plot them along with dopaminergic markers, and using all features and cells before filtering (otherwise only TBR1 remains)
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## Warning in getVarianceExplained(dummy, variables = variables, exprs_values
## = "pc_space", : ignoring 'Type' with fewer than 2 unique levels
## Warning in getVarianceExplained(dummy, variables = variables, exprs_values
## = "pc_space", : ignoring 'Project' with fewer than 2 unique levels
## Warning in getVarianceExplained(dummy, variables = variables, exprs_values
## = "pc_space", : ignoring 'Genome' with fewer than 2 unique levels
## Warning in getVarianceExplained(dummy, variables = variables, exprs_values
## = "pc_space", : ignoring 'sex' with fewer than 2 unique levels
## Warning in getVarianceExplained(dummy, variables = variables, exprs_values
## = "pc_space", : ignoring 'reprogramming' with fewer than 2 unique levels
## Warning in getVarianceExplained(dummy, variables = variables, exprs_values
## = "pc_space", : ignoring 'is_cell_control' with fewer than 2 unique levels
## Warning in getVarianceExplained(dummy, variables = variables, exprs_values
## = "pc_space", : ignoring 'is_cell_control_control' with fewer than 2 unique
## levels
## Warning in getVarianceExplained(dummy, variables = variables, exprs_values
## = "pc_space", : ignoring 'pct_counts_in_top_50_features_ERCCs' with fewer
## than 2 unique levels
## Warning in getVarianceExplained(dummy, variables = variables, exprs_values
## = "pc_space", : ignoring 'pct_counts_in_top_100_features_ERCCs' with fewer
## than 2 unique levels
## Warning in getVarianceExplained(dummy, variables = variables, exprs_values
## = "pc_space", : ignoring 'pct_counts_in_top_200_features_ERCCs' with fewer
## than 2 unique levels
## Warning in getVarianceExplained(dummy, variables = variables, exprs_values
## = "pc_space", : ignoring 'pct_counts_in_top_500_features_ERCCs' with fewer
## than 2 unique levels
## Warning in getVarianceExplained(dummy, variables = variables, exprs_values
## = "pc_space", : ignoring 'PC1_k2_cluster' with fewer than 2 unique levels
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## 'colour', which will replace the existing scale.
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## Warning in getVarianceExplained(dummy, variables = variables, exprs_values
## = "pc_space", : ignoring 'Type' with fewer than 2 unique levels
## Warning in getVarianceExplained(dummy, variables = variables, exprs_values
## = "pc_space", : ignoring 'Project' with fewer than 2 unique levels
## Warning in getVarianceExplained(dummy, variables = variables, exprs_values
## = "pc_space", : ignoring 'Genome' with fewer than 2 unique levels
## Warning in getVarianceExplained(dummy, variables = variables, exprs_values
## = "pc_space", : ignoring 'sex' with fewer than 2 unique levels
## Warning in getVarianceExplained(dummy, variables = variables, exprs_values
## = "pc_space", : ignoring 'reprogramming' with fewer than 2 unique levels
## Warning in getVarianceExplained(dummy, variables = variables, exprs_values
## = "pc_space", : ignoring 'is_cell_control' with fewer than 2 unique levels
## Warning in getVarianceExplained(dummy, variables = variables, exprs_values
## = "pc_space", : ignoring 'is_cell_control_control' with fewer than 2 unique
## levels
## Warning in getVarianceExplained(dummy, variables = variables, exprs_values
## = "pc_space", : ignoring 'pct_counts_in_top_50_features_ERCCs' with fewer
## than 2 unique levels
## Warning in getVarianceExplained(dummy, variables = variables, exprs_values
## = "pc_space", : ignoring 'pct_counts_in_top_100_features_ERCCs' with fewer
## than 2 unique levels
## Warning in getVarianceExplained(dummy, variables = variables, exprs_values
## = "pc_space", : ignoring 'pct_counts_in_top_200_features_ERCCs' with fewer
## than 2 unique levels
## Warning in getVarianceExplained(dummy, variables = variables, exprs_values
## = "pc_space", : ignoring 'pct_counts_in_top_500_features_ERCCs' with fewer
## than 2 unique levels
## Warning in getVarianceExplained(dummy, variables = variables, exprs_values
## = "pc_space", : ignoring 'PC1_k2_cluster' with fewer than 2 unique levels
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning
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